To maintain communications networks operating at an acceptable performance level, it can be necessary to frequently monitor and assess the performance of such networks. One common way of assessing the performance of a network is by using objective criteria to examine the received signal and the originally sent signal, and determining to what extent the originally sent signal has been degraded. Objective criteria such as signal level, echo, signal-to-noise ratio, packet-loss rate (in the case of packet-based networks) etc., thus may provide some indication of the performance of the network in question. However, such objective criteria do not always correlate well with the quality of the transmitted communications as perceived by human network users. Accordingly, several methods incorporating mathematical models of the human sensory and perceptual systems, and taking into account typical sources of signal corruptions, automatically assess (i.e., without using live human subjects) the perceptual quality of signals, and by extension, the performance of transmission systems. Amongst such known methods are the Perceptual Analysis Measurement System (or PAMS), the Perceptual Evaluation of Speech Quality (or PESQ) algorithm/scheme, and others. Disadvantageously, these performance evaluation methods, much like objective criteria based schemes, require a reference signal against which the received signal is compared. Such methods that process and compare the received signal to the uncorrupted input signal are often referred to as “input/output-based methods.” Consequently, to evaluate network performance using input/output-based signal quality assessment schemes, a known test signal has to be transmitted through the network and then compared at the received end to the uncorrupted version of that test signal, or alternatively, the original uncorrupted version of the signal measured at the receiving end has to be provided at the receiving end to compare it to the corrupted received signal.
To overcome the need to have a reference signal available for the purpose of comparing to the received signal, several known methods have been developed that process only the received signal, and return a value that is indicative of the perceived quality of the signal. These types of quality assessment methods, which take as input only the received signal, are often referred to as “output-based.” One example of a commercially available output-based application to perform voice-quality testing is 3SQM™ developed by Opticom GmbH of Germany. Although certain inferences about the performance of a network can be made from measurements produced by such output-based methods, such measurements can be unsatisfactory for the purpose of obtaining accurate information about the performance of a network since the produced measurements can depend on the original form of the transmitted signal. For example, an output-based quality measurement method may produce different assessments depending on the particular speech signal being measured, even though such speech signals are traversing the same network (and may therefore be subject to the same degradation).